class_docstr: Atari100k Ranibow Agent with recycled parameters.
class_docstr_tok: ["Atari100k","Ranibow","Agent","with","recycled","parameters","."]
code: def get_intermediates(self, online_params):
      # TODO(gsokar) add a check if batch_size equals batch_size_statistics
      # then no need to sample a new batch from buffer.
      batch = self._sample_batch_for_statistics()
  
      def apply_data(x):
          filter_rep = (
              lambda l, _: l.name is not None and "act" in l.name
          )
          self._rng, key = jax.random.split(self._rng)
          return self.network_def.apply(
              online_params,
              x,
              support=self._support,
              key=key,
              capture_intermediates=filter_rep,
              mutable=["intermediates"],
          )
  
      _, state = jax.vmap(apply_data)(batch)
      return state["intermediates"]
  
file_path: dopamine/dopamine/labs/redo/recycled_atari100k_rainbow_agent.py
imports: from dopamine.jax.agents.full_rainbow import full_rainbow_agent
  from dopamine.labs.atari_100k import atari_100k_rainbow_agent
  from dopamine.labs.redo import networks
  from dopamine.labs.redo import weight_recyclers
  from dopamine.metrics import statistics_instance
  import gin
  import jax
  import jax.numpy
  import optax
label_desc: Use image node_id 7 for calling the RecycledAtari100kRainbowAgent obj's underlying member method code with example usage: obj.get_intermediates(online_params) and returns: state, self